Optimal inference of molecular interaction dynamics in FRET microscopy

Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the obser...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2023-04, Vol.120 (15), p.e2211807120-e2211807120
Main Authors: Kamino, Keita, Kadakia, Nirag, Avgidis, Fotios, Liu, Zhe-Xuan, Aoki, Kazuhiro, Shimizu, Thomas S, Emonet, Thierry
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian analysis
Biological Sciences
Energy transfer
Fluorescence
Fluorescence resonance energy transfer
Fluorescence Resonance Energy Transfer - methods
Inverse problems
Microscopy
Molecular interactions
Noise measurement
Noise reduction
Photobleaching
Physical Sciences
Signal to noise ratio
Time series
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Summary:Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible-a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.2211807120